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Assignment of structural behaviours in long-term monitoring: Application to a strengthened railway bridge

机译：长期监测中的结构行为分配：在加固铁路桥梁中的应用

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摘要

Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004-2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge's dynamic behaviour over time. To this end, the symbolic data analysis-based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviour.
机译：新颖性检测，即识别异常或不同数据的方法，在许多现实世界中都具有重要意义，范围从识别异常天气情况到检测机械系统损坏的证据。使用新颖性检测方法进行结构健康监测给非专业用户带来了巨大挑战。在本文中，引入了符号数据分析来建模测试中的变异性。然后使用分层划分方法和动态云过程来区分结构变化，这些变化用作对结构行为进行分类的新颖性检测方法。本文报道了在法国铁路桥梁上进行的实验测试的研究。这座桥在2003年夏季进行了加固工程。从2004年至2006年，记录了新的动态测试集。主要目的是分析桥梁动力行为随时间的演变。为此，基于符号数据分析的聚类方法用于将新测试分配给在强化前后识别出的聚类，或者突出显示完全不同的结构行为。

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